# import numpy as np
# A=np.mat("1,-1,1;2,1,0;2,1,-1")
# b=np.array([4,3,-1])
# x=np.linalg.solve(A,b)
# print(x)
# import numpy as np
# A=np.array([[1,2],[3,4]])
# B=np.array([[5,6],[7,8]])
# C_s=np.hstack((A,B))
# C_v=np.vstack((A,B))
# print(C_s)
# # print(C_v)
# # import pandas as pd
# # import numpy as np
# # dict1={'a':[2,2,'kt',6],'b':[4,6,7,8],'c':[6,5,np.nan,6]}
# # dict2={'d':[8,9,10,11],'e':['p',16,10,8]}
# # dict3={'a':[1,2],'b':[2,3],'c':[3,4],'d':[4,5],'e':[5,6]}
# # df1=pd.DataFrame(dict1)
# # df2=pd.DataFrame(dict2)
# # df3=pd.DataFrame(dict3)
# # del dict1,dict2,dict3
# # df4=pd.concat([df1,df2],axis=1)
# # df5=pd.concat([df3,df4],axis=0)
# # df5.index=range(6)
# # print(df1)
# # print(df2)
# # print(df3)
# # print(df4)
# # print(df5)
# import pandas as pd
# pd=pd.read_table('txt1.txt',sep=',')
# print(pd)
# pd1=pd.iloc[0:3]
# pd2=pd.iloc[3:6]
# pd3=pd.iloc[6:9]
# pd4=pd.iloc[9:12]
# print(pd1)
# print(pd2)
# print(pd3)
# print(pd4)
# import pandas as pd
# data=pd.read_excel('data3.xlsx')
# x=data.iloc[:,0:4]
# y=data.iloc[:,4]
# from sklearn.neural_network import MLPRegressor
# clf=MLPRegressor(solver='lbfgs',alpha=1e-5,hidden_layer_sizes=(300,5),random_state=1)
# clf.fit(x,y);
# rvl=clf.score(x,y)
# import numpy as np
# x1=np.array([28.4,50.6,1011.9,80.54])
# x1=x1.reshape(1,4)
# R=clf.predict(x1)
# print('样本预测值为：',R)
#
import numpy as np